




"""simple docstring"""




import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int



def        a__					(							snake_case__			,					snake_case__							)		->   List[str]:
			if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
						# expert layer
						lowerCamelCase      =   flax_key_tuple[:-1] + ("""weight""",)
						lowerCamelCase      =   torch.permute(snake_case__			,					(0, 2, 1)							)
			elif flax_key_tuple[-1] == "kernel" and ".".join(snake_case__							):
						# linear layer
						lowerCamelCase      =   flax_key_tuple[:-1] + ("""weight""",)
						lowerCamelCase      =   flax_tensor.T
			elif flax_key_tuple[-1] in ["scale", "embedding"]:
						lowerCamelCase      =   flax_key_tuple[:-1] + ("""weight""",)

			return flax_key_tuple, flax_tensor



def        a__					(							snake_case__			,					snake_case__			,					snake_case__							)		->   int:
			if "metadata" in layer:
						lowerCamelCase      =   layer.split("""metadata"""							)
						lowerCamelCase      =   """""".join(split_layer[0]							)[:-1]
						lowerCamelCase      =   [tuple(("""metadata""" + split_layer[1]).split("""/"""							)							)]
			elif "kvstore" in layer:
						lowerCamelCase      =   layer.split("""kvstore"""							)
						lowerCamelCase      =   """""".join(split_layer[0]							)[:-1]
						lowerCamelCase      =   [tuple(("""kvstore""" + split_layer[1]).split("""/"""							)							)]

			else:
						lowerCamelCase      =   layer.split("""/"""							)
						lowerCamelCase      =   """/""".join(split_layer[:-1]							)
						lowerCamelCase      =   (split_layer[-1],)

			if "kvstore/path" in layer:
						lowerCamelCase      =   F'{switch_checkpoint_path}/{checkpoint_info[layer]}'
			elif "kvstore/driver" in layer:
						lowerCamelCase      =   """file"""
			else:
						lowerCamelCase      =   checkpoint_info[layer]

			return curr_real_layer_name, split_layer, content



def        a__					(							snake_case__			,					snake_case__							)		->   Any:
			lowerCamelCase      =   rename_keys(snake_case__							)
			lowerCamelCase      =   {}
			for k, v in current_block.items():
						lowerCamelCase      =   v
			lowerCamelCase      =   new_current_block
			torch.save(snake_case__			,					snake_case__							)



def        a__					(							snake_case__			,					snake_case__			,					snake_case__			,					snake_case__			,					snake_case__ = WEIGHTS_NAME							)		->   Optional[Any]:
			lowerCamelCase      =   convert_file_size_to_int(snake_case__							)
			lowerCamelCase      =   []
			lowerCamelCase      =   {}
			lowerCamelCase      =   0
			lowerCamelCase      =   0

			os.makedirs(snake_case__			,					exist_ok=snake_case__							)
			with gfile.GFile(switch_checkpoint_path + """/checkpoint"""			,					"""rb"""							) as fp:
						lowerCamelCase      =   serialization.msgpack_restore(fp.read()							)["""optimizer"""]["""target"""]
						lowerCamelCase      =   flatten_dict(snake_case__			,					sep="""/"""							)

			lowerCamelCase      =   {}
			for layer in checkpoint_info.keys():
						lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase      =   get_key_and_tensorstore_dict(
						    snake_case__			,					snake_case__			,					snake_case__							)
						if curr_real_layer_name in all_layers:
									lowerCamelCase      =   content
						else:
									lowerCamelCase      =   {split_layer[-1]: content}

			for key in all_layers.keys():
						# open tensorstore file
						lowerCamelCase      =   ts.open(unflatten_dict(all_layers[key]							)							).result().read().result()
						lowerCamelCase      =   torch.tensor(snake_case__							)
						lowerCamelCase      =   raw_weights.numel() * dtype_byte_size(raw_weights.dtype							)

						# use the renaming pattern from the small conversion scripts
						lowerCamelCase ,  lowerCamelCase      =   rename_base_flax_keys(tuple(key.split("""/"""							)							)			,					snake_case__							)
						lowerCamelCase      =   """/""".join(snake_case__							)

						# If this weight is going to tip up over the maximal size, we split.
						if current_block_size + weight_size > max_shard_size:
									lowerCamelCase      =   os.path.join(
									    snake_case__			,					weights_name.replace(""".bin"""			,					F'-{len(snake_case__							)+1:05d}-of-???.bin'							)							)
									rename_and_save_block(snake_case__			,					snake_case__							)
									sharded_state_dicts.append(current_block.keys()							)
									del current_block
									lowerCamelCase      =   {}
									lowerCamelCase      =   0

						lowerCamelCase      =   raw_weights.to(getattr(snake_case__			,					snake_case__							)							)
						current_block_size += weight_size
						total_size += weight_size

			# Add the last block
			lowerCamelCase      =   os.path.join(snake_case__			,					weights_name.replace(""".bin"""			,					F'-{len(snake_case__							)+1:05d}-of-???.bin'							)							)
			rename_and_save_block(snake_case__			,					snake_case__							)
			sharded_state_dicts.append(current_block.keys()							)

			# If we only have one shard, we return it
			if len(snake_case__							) == 1:
						return {weights_name: sharded_state_dicts[0]}, None

			# Otherwise, let's build the index
			lowerCamelCase      =   {}
			lowerCamelCase      =   {}
			for idx, shard in enumerate(snake_case__							):
						lowerCamelCase      =   weights_name.replace(
						    """.bin"""			,					F'-{idx+1:05d}-of-{len(snake_case__							):05d}.bin'							)  # len(sharded_state_dicts):05d}
						lowerCamelCase      =   os.path.join(snake_case__			,					weights_name.replace(""".bin"""			,					F'-{idx+1:05d}-of-???.bin'							)							)
						os.rename(snake_case__			,					os.path.join(snake_case__			,					snake_case__							)							)
						lowerCamelCase      =   shard
						for key in shard:
									lowerCamelCase      =   shard_file

    # Add the metadata
			lowerCamelCase      =   {"""total_size""": total_size}
			lowerCamelCase      =   {"""metadata""": metadata, """weight_map""": weight_map}

			with open(os.path.join(snake_case__			,					snake_case__							)			,					"""w"""			,					encoding="""utf-8"""							) as f:
						lowerCamelCase      =   json.dumps(snake_case__			,					indent=2			,					sort_keys=snake_case__							) + """\n"""
						f.write(snake_case__							)

			return metadata, index


if __name__ == "__main__":
					lowerCAmelCase				:     Any					=				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--switch_t5x_checkpoint_path""",
					    default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600""",
					    type=str,
					    required=False,
					    help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
					)
					parser.add_argument("""--max_shard_size""", default="""10GB""", required=False, help="""Max shard size""")
					parser.add_argument("""--dtype""", default="""bfloat16""", type=str, required=False, help="""dtype of the saved model""")
					parser.add_argument(
					    """--pytorch_dump_folder_path""",
					    default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted""",
					    type=str,
					    required=False,
					    help="""Path to the output pytorch model.""",
					)
					lowerCAmelCase				:     List[Any]					=				parser.parse_args()
					shard_on_the_fly(
					    args.switch_tax_checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.max_shard_size,
					    args.dtype,
					)



def        a__					(							)		->   List[str]:
			from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

			lowerCamelCase      =   SwitchTransformersConfig.from_pretrained("""google/switch-base-8"""							)
			config.save_pretrained("""/home/arthur_huggingface_co/transformers/switch_converted"""							)
			lowerCamelCase      =   SwitchTransformersForConditionalGeneration.from_pretrained(
			    """/home/arthur_huggingface_co/transformers/switch_converted"""			,					device_map="""auto"""							)

			lowerCamelCase      =   TaTokenizer.from_pretrained("""t5-small"""							)
			lowerCamelCase      =   """A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."""

			lowerCamelCase      =   tokenizer(snake_case__			,					return_tensors="""pt"""							).input_ids
			lowerCamelCase      =   model.generate(snake_case__			,					decoder_start_token_id=0							)
			print(tokenizer.decode(out[0]							)							)









"""simple docstring"""




def        a__					(							snake_case__							)		->   list:

			if len(snake_case__							) <= 1:
						return [tuple(snake_case__							)]

			lowerCamelCase      =   []

			def generate(snake_case__			,					snake_case__							):
						lowerCamelCase      =   [0] * n
						res.append(tuple(snake_case__							)							)

						lowerCamelCase      =   0
						while i < n:
									if c[i] < i:
												if i % 2 == 0:
															lowerCamelCase ,  lowerCamelCase      =   arr[i], arr[0]
												else:
															lowerCamelCase ,  lowerCamelCase      =   arr[i], arr[c[i]]
												res.append(tuple(snake_case__							)							)
												c[i] += 1
												lowerCamelCase      =   0
									else:
												lowerCamelCase      =   0
												i += 1

			generate(len(snake_case__							)			,					snake_case__							)
			return res


if __name__ == "__main__":
					lowerCAmelCase				:     str					=				input("""Enter numbers separated by a comma:\n""").strip()
					lowerCAmelCase				:     List[Any]					=				[int(item) for item in user_input.split(""",""")]
					print(heaps(arr))



